Automobiles extend people's travel range, provide travel convenience to people and improve people's quality of life. With the development and progress of science and technology, driverless vehicles controlled by intelligent systems have become an important trend in future automobile development because they can acquire more driving information than manned vehicles and have higher security.
Driverless vehicles use a robot operating system to perform information transmission, and rely on the collaboration of an artificial intelligence module, a visual computing module, a video camera module, a radar sensor module, a laser radar module, and a Global Positioning System (GPS) module, so that the driverless vehicles can automatically and safely travel with no assistance.
However, there are still some shortcomings in processing data in the existing driverless vehicles. A driverless vehicle generally includes a sensor processing node, a perceptual computing node, a decision and control node, etc. The types of the data directly transmitted by the nodes are usually different. There are generally two methods for implementing data transmission or data transcoding between the nodes. In a first method, a data transcoding module is arranged between any two nodes. When there are a large number of nodes, a plurality of data transcoding modules are required. In a second method, a plurality of data output ports is arranged at a node output port for subsequent receipt of designated types of data at the node. These two methods either add numerous additional modules, increasing the probability of errors in the data transmission process, or consume more data processing capacity at each node, increasing the data traffic, and having no possibility to change the encoding of the to be transmitted data in time when the encoding format or encoding rule changes, eventually reducing the information transmission efficiency and the transcoding accuracy of the driverless vehicle.